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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.test.util</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import os
import shutil
import tempfile

import torch


class OverwriteTensorEquality:
    &#34;&#34;&#34;Convenient tool for overwriting torch.Tensor&#39;s (in)equality functions.

    Examples
    --------

    ```python
    import torch
    from silk.test.util import OverwriteTensorEquality

    tensor = torch.tensor([1, 2, 3])

    if tensor == tensor: # raise exception
        print(&#34;not executed&#34;)
    # &gt;&gt;&gt; RuntimeError: Boolean value of Tensor with more than one value is ambiguous

    with OverwriteTensorEquality(torch):
        if tensor == tensor: # doesn&#39;t raise exception
            print(&#34;executed&#34;)
    # &gt;&gt;&gt; executed
    ```

    &#34;&#34;&#34;

    _OVERWRITEN = set()

    def __init__(
        self, torch_module, check_shape=False, check_dtype=False, check_device=False
    ) -&gt; None:
        &#34;&#34;&#34;

        Parameters
        ----------
        torch_module : module
            Torch module to overwrite the torch.Tensor functions from.
        &#34;&#34;&#34;

        # do not overwrite methods twice
        if torch_module.Tensor in OverwriteTensorEquality._OVERWRITEN:
            self._torch_module = None
        else:
            self._torch_module = torch_module

            OverwriteTensorEquality._OVERWRITEN.add(self._torch_module.Tensor)

            self._pytorch_tensor_eq = self._torch_module.Tensor.__eq__
            self._pytorch_tensor_ne = self._torch_module.Tensor.__ne__

        self._check_shape = check_shape
        self._check_dtype = check_dtype
        self._check_device = check_device

    def _test_tensor_eq(self):
        def eq(t0, t1):
            if self._check_shape and t0.shape != t1.shape:
                return False
            if self._check_dtype and t0.dtype != t1.dtype:
                return False
            if self._check_device and t0.device != t1.device:
                return False

            return self._pytorch_tensor_eq(t0, t1).all()

        return eq

    def _test_tensor_ne(self):
        eq = self._test_tensor_eq()

        def ne(t0, t1):
            return not eq(t0, t1)

        return ne

    def __enter__(self):
        if self._torch_module is not None:
            self._torch_module.Tensor.__eq__ = self._test_tensor_eq()
            self._torch_module.Tensor.__ne__ = self._test_tensor_ne()
        return self

    def __exit__(self, type, value, traceback):
        if self._torch_module is not None:
            OverwriteTensorEquality._OVERWRITEN.remove(self._torch_module.Tensor)

            self._torch_module.Tensor.__eq__ = self._pytorch_tensor_eq
            self._torch_module.Tensor.__ne__ = self._pytorch_tensor_ne


def string_to_tensor(string: str, *args, **kwargs):
    array = []
    for line in string.split(&#34;\n&#34;):
        elements = line.split(&#34;,&#34;)
        if len(elements) &gt; 1:
            array.append([float(el) for el in line.split(&#34;,&#34;) if len(el.strip()) &gt; 0])
    return torch.tensor(array, *args, **kwargs)


def temporary_file():
    &#34;&#34;&#34;Automatically creates a temporary file (for testing purposes) and cleans it once it gets out of scope.

    Examples
    --------

    ```python
    from silk.test.util import temporary_file

    with temporary_file() as filepath:
        ... # do something with file at &#34;filepath&#34;

    # here the file &#34;filepath&#34; has been removed
    ```

    &#34;&#34;&#34;
    return _TemporaryFile()


class _TemporaryFile:
    def __enter__(self):
        fd, self.filepath = tempfile.mkstemp()
        os.close(fd)
        return self.filepath

    def __exit__(self, type, value, traceback):
        if os.path.isfile(self.filepath):
            os.remove(self.filepath)


def temporary_directory():
    &#34;&#34;&#34;Automatically creates a temporary directory (for testing purposes) and cleans it once it gets out of scope.

    Examples
    --------

    ```python
    from silk.test.util import temporary_directory

    with temporary_directory() as dirpath:
        ... # do something with directory at &#34;dirpath&#34;

    # here the directory &#34;dirpath&#34; has been removed
    ```

    &#34;&#34;&#34;
    return _TemporaryDirectory()


class _TemporaryDirectory:
    def __enter__(self):
        self.dirpath = tempfile.mkdtemp()
        return self.dirpath

    def __exit__(self, type, value, traceback):
        if os.path.isdir(self.dirpath):
            shutil.rmtree(self.dirpath)


def max_tensor_diff(tensor0, tensor1):
    return torch.max(torch.abs(tensor0 - tensor1)).detach().cpu().item()</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.test.util.max_tensor_diff"><code class="name flex">
<span>def <span class="ident">max_tensor_diff</span></span>(<span>tensor0, tensor1)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def max_tensor_diff(tensor0, tensor1):
    return torch.max(torch.abs(tensor0 - tensor1)).detach().cpu().item()</code></pre>
</details>
</dd>
<dt id="silk.test.util.string_to_tensor"><code class="name flex">
<span>def <span class="ident">string_to_tensor</span></span>(<span>string: str, *args, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def string_to_tensor(string: str, *args, **kwargs):
    array = []
    for line in string.split(&#34;\n&#34;):
        elements = line.split(&#34;,&#34;)
        if len(elements) &gt; 1:
            array.append([float(el) for el in line.split(&#34;,&#34;) if len(el.strip()) &gt; 0])
    return torch.tensor(array, *args, **kwargs)</code></pre>
</details>
</dd>
<dt id="silk.test.util.temporary_directory"><code class="name flex">
<span>def <span class="ident">temporary_directory</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"><p>Automatically creates a temporary directory (for testing purposes) and cleans it once it gets out of scope.</p>
<h2 id="examples">Examples</h2>
<pre><code class="language-python">from silk.test.util import temporary_directory

with temporary_directory() as dirpath:
    ... # do something with directory at &quot;dirpath&quot;

# here the directory &quot;dirpath&quot; has been removed
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def temporary_directory():
    &#34;&#34;&#34;Automatically creates a temporary directory (for testing purposes) and cleans it once it gets out of scope.

    Examples
    --------

    ```python
    from silk.test.util import temporary_directory

    with temporary_directory() as dirpath:
        ... # do something with directory at &#34;dirpath&#34;

    # here the directory &#34;dirpath&#34; has been removed
    ```

    &#34;&#34;&#34;
    return _TemporaryDirectory()</code></pre>
</details>
</dd>
<dt id="silk.test.util.temporary_file"><code class="name flex">
<span>def <span class="ident">temporary_file</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"><p>Automatically creates a temporary file (for testing purposes) and cleans it once it gets out of scope.</p>
<h2 id="examples">Examples</h2>
<pre><code class="language-python">from silk.test.util import temporary_file

with temporary_file() as filepath:
    ... # do something with file at &quot;filepath&quot;

# here the file &quot;filepath&quot; has been removed
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def temporary_file():
    &#34;&#34;&#34;Automatically creates a temporary file (for testing purposes) and cleans it once it gets out of scope.

    Examples
    --------

    ```python
    from silk.test.util import temporary_file

    with temporary_file() as filepath:
        ... # do something with file at &#34;filepath&#34;

    # here the file &#34;filepath&#34; has been removed
    ```

    &#34;&#34;&#34;
    return _TemporaryFile()</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.test.util.OverwriteTensorEquality"><code class="flex name class">
<span>class <span class="ident">OverwriteTensorEquality</span></span>
<span>(</span><span>torch_module, check_shape=False, check_dtype=False, check_device=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Convenient tool for overwriting torch.Tensor's (in)equality functions.</p>
<h2 id="examples">Examples</h2>
<pre><code class="language-python">import torch
from silk.test.util import OverwriteTensorEquality

tensor = torch.tensor([1, 2, 3])

if tensor == tensor: # raise exception
    print(&quot;not executed&quot;)
# &gt;&gt;&gt; RuntimeError: Boolean value of Tensor with more than one value is ambiguous

with OverwriteTensorEquality(torch):
    if tensor == tensor: # doesn't raise exception
        print(&quot;executed&quot;)
# &gt;&gt;&gt; executed
</code></pre>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>torch_module</code></strong> :&ensp;<code>module</code></dt>
<dd>Torch module to overwrite the torch.Tensor functions from.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class OverwriteTensorEquality:
    &#34;&#34;&#34;Convenient tool for overwriting torch.Tensor&#39;s (in)equality functions.

    Examples
    --------

    ```python
    import torch
    from silk.test.util import OverwriteTensorEquality

    tensor = torch.tensor([1, 2, 3])

    if tensor == tensor: # raise exception
        print(&#34;not executed&#34;)
    # &gt;&gt;&gt; RuntimeError: Boolean value of Tensor with more than one value is ambiguous

    with OverwriteTensorEquality(torch):
        if tensor == tensor: # doesn&#39;t raise exception
            print(&#34;executed&#34;)
    # &gt;&gt;&gt; executed
    ```

    &#34;&#34;&#34;

    _OVERWRITEN = set()

    def __init__(
        self, torch_module, check_shape=False, check_dtype=False, check_device=False
    ) -&gt; None:
        &#34;&#34;&#34;

        Parameters
        ----------
        torch_module : module
            Torch module to overwrite the torch.Tensor functions from.
        &#34;&#34;&#34;

        # do not overwrite methods twice
        if torch_module.Tensor in OverwriteTensorEquality._OVERWRITEN:
            self._torch_module = None
        else:
            self._torch_module = torch_module

            OverwriteTensorEquality._OVERWRITEN.add(self._torch_module.Tensor)

            self._pytorch_tensor_eq = self._torch_module.Tensor.__eq__
            self._pytorch_tensor_ne = self._torch_module.Tensor.__ne__

        self._check_shape = check_shape
        self._check_dtype = check_dtype
        self._check_device = check_device

    def _test_tensor_eq(self):
        def eq(t0, t1):
            if self._check_shape and t0.shape != t1.shape:
                return False
            if self._check_dtype and t0.dtype != t1.dtype:
                return False
            if self._check_device and t0.device != t1.device:
                return False

            return self._pytorch_tensor_eq(t0, t1).all()

        return eq

    def _test_tensor_ne(self):
        eq = self._test_tensor_eq()

        def ne(t0, t1):
            return not eq(t0, t1)

        return ne

    def __enter__(self):
        if self._torch_module is not None:
            self._torch_module.Tensor.__eq__ = self._test_tensor_eq()
            self._torch_module.Tensor.__ne__ = self._test_tensor_ne()
        return self

    def __exit__(self, type, value, traceback):
        if self._torch_module is not None:
            OverwriteTensorEquality._OVERWRITEN.remove(self._torch_module.Tensor)

            self._torch_module.Tensor.__eq__ = self._pytorch_tensor_eq
            self._torch_module.Tensor.__ne__ = self._pytorch_tensor_ne</code></pre>
</details>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.test" href="index.html">silk.test</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.test.util.max_tensor_diff" href="#silk.test.util.max_tensor_diff">max_tensor_diff</a></code></li>
<li><code><a title="silk.test.util.string_to_tensor" href="#silk.test.util.string_to_tensor">string_to_tensor</a></code></li>
<li><code><a title="silk.test.util.temporary_directory" href="#silk.test.util.temporary_directory">temporary_directory</a></code></li>
<li><code><a title="silk.test.util.temporary_file" href="#silk.test.util.temporary_file">temporary_file</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.test.util.OverwriteTensorEquality" href="#silk.test.util.OverwriteTensorEquality">OverwriteTensorEquality</a></code></h4>
</li>
</ul>
</li>
</ul>
</nav>
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